The endogenous grid method for discrete-continuous dynamic choice models with (or without) taste shocks (original) (raw)

Estimation of Dynastic Life-cycle Discrete Choice Models

This paper explores the estimation of a class of life-cycle discrete choice intergenerational models. It proposes a new semi-parametric estimator. It shows that it is root N-consistent and asymptotically normally distributed. We compare our estimator with a modi ed version of the full solution maximum likelihood estimator (MLE) in a Monte Carlo study. Our estimator performs comparably to the MLE in a finite sample but greatly reduces the computational cost. The paper documents that the quantity-quality trade-o¤s depend on the household composition and specialization in the household. Using the proposed estimator, we estimate a dynastic model that rationalizes these observed patterns.

Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models

2020

In nonlinear panel data models, fixed effects methods are often criticized because they cannot identify average marginal effects (AMEs) in short panels. The common argument is that the identification of AMEs requires knowledge of the distribution of unobserved heterogeneity, but this distribution is not identified in a fixed effects model with a short panel. In this paper, we derive identification results that contradict this argument. In a panel data dynamic logic model, and for T as small as four, we prove the point identification of different AMEs, including causal effects of changes in the lagged dependent variable or in the duration in last choice. Our proofs are constructive and provide simple closed-form expressions for the AMEs in terms of probabilities of choice histories. We illustrate our results using Monte Carlo experiments and with an empirical application of a dynamic structural model of consumer brand choice with state dependence.